Data Science - Master of Science Degree Programme

A master's degree in Data Science makes you eligible for the most demanding and interesting work tasks within data analysis, smart solutions (such as smart cities, smart energy), and digitalization.

Programme content, structure and composition

The University of Stavanger offers a master's programme aimed at students who have completed a 3-year engineering degree or similar with necessary background in programming and computer science (at least 20 ECTS). The two-year master's degree in Data Science comprises 120 ECTS.
 
The programme has practical courses that build on mathematics, statistics, and basic computer science courses from the bachelor's degree. The programme contains advanced statistical topics, processing of large dataset, Cloud solutions, machine learning, and data mining.

 
The programme offers a variety of work and teaching activities, from traditional lecture series and exercises, project work, self-study and laboratory teaching to introduction and practice in the use of modern software. The emphasis on the individual teaching forms varies to some extent between the different subject groups.
 
The following is described in the individual course description:

         • Forms of work and teaching

         • Evaluation Forms

         • Syllabus

         • Assessment

 
The university aims to offer all the study programs as planned but must make reservations about sufficient resources and / or students to complete the offer. Over time, it will be natural for the academic content and offering of courses to change due to the general developments in the field of study, the use of technology and changes in society at large.
 
After admission to the programme, you can apply for a part-time study programme. Alternatively, you can apply directly to a part-time study.

Learning outcomes

After having completed the master’s programme in Data Science, the student shall have acquired the following learning outcomes, in terms of knowledge, skills and general competences:

Knowledge

K1: Advanced knowledge within Data Science, which includes data processing, machine learning, data extraction, statistics and typical programming languages for the area, including: Pythonand R.

K2: Specialised insightinto data analysis.

K3: In-depth knowledge of scientific theory and methods in Data Science.

K4: Apply knowledge about algorithms for statistical analysis, machine learning or data extraction in new areas within data science.

K5: Analyse professional issues based on the fourth science paradigm, 4Vs of big data (volume, velocity, variety, and variability), data-driven approach, CRISP-DM (cross-industry standard process for data mining).

Skills

S1: Analyseand relate critically to different sources of information, datasets and data processes; and apply these to structure and formulate data-driven reasoning.

S2: Analyse existing theories, methods and interpretations within the subject area and work independently in applying and evaluating different storage and data processing technologies.

S3: Use CRISP-DM and scientific methods to develop data analysis programs in an independent way.

S4: Conduct independent, limited data collection, analysis and evaluation according to established engineering principles in accordance with current research ethical standards.

General Competence

G1: Analyse relevant ethical issues arising from data usage and data recovery.

G2: Apply theirknowledge and skills in new areas to carry out advanced tasks and projects related to data processing, data analysis and optimisation.

G3: Communicate results of comprehensive data analysis and development work, and master Data Science expressions.

G4: Communicate on issues, analyses and conclusions related to data-driven research and development, both with specialists and to the general public.

G5: Contribute to new ideas and innovation processes by introducing data-driven approaches, comprehensive data analysis and development work, and master Data Science expressions.

Career prospects

With a master’s degree in Data Science, you can get a position in almost all industries. Some examples of businesses where you can find employment are consulting companies, telecommunications companies, energy related businesses, hospitals, and other public agencies. Specialisation in Data Science provides a basis for work in data analysis and development of data processing systems for the whole data lifecycle. It builds knowledge and skills in advanced statistics, data mining, machine learning and processing of large data volumes. 

Completed master’s degree in Data Science provides the basis for admission the PhD programme in Information technology, mathematics and physics.

Course assessment

Schemes for quality assurance and evaluation of studies are stipulated in the Quality system for education